# BioArtlas: Computational Clustering of Multi-Dimensional Complexity in Bioart

- Authors: Joonhyung Bae
- Venue: Proc. 39th Annual Conference on Neural Information Processing Systems Creative AI Track, San Diego, CA, USA, Dec. 2-7, 2025
- Year: 2025
- Type: Conference Paper
- arXiv: https://arxiv.org/abs/2511.19162
- PDF: https://drive.google.com/file/d/1cOFNX9_fSElhr82nEqz4eceoduLoBPeF/view?usp=drive_link
- Keywords: bioart, computational clustering, machine learning, creative AI, data visualization
- Page: https://joonhyungbae.com/publication/bioartlas-2025

## In one sentence

A computational atlas that represents 81 bioartworks along 13 interpretive axes and clusters the field by conceptual similarity rather than medium or chronology.

바이오아트 81점을 13개 해석 축으로 동시에 표현하고 개념적 유사성으로 군집화한 계산적 아틀라스. 매체·연대가 아니라 개념으로 분야를 지도화한다.

## Abstract

Bioart brings living material into artistic practice, where a single work can be at once an aesthetic object, a scientific instrument, and an ethical provocation. Traditional categories sort such works along one axis at a time, which flattens the very hybridity that defines the field and leaves curators no way to compare works across many dimensions together. I introduce BioArtlas, a computational atlas that represents each bioartwork along many curated dimensions at once and organizes the field by conceptual similarity rather than by medium or chronology. My method embeds the keywords of all 81 works on each of thirteen interpretive axes, groups related concepts into a shared codebook that tames inconsistent terminology, and then searches systematically for a clustering that is both statistically clean and interpretable. Among the methods that place every work on the map, agglomerative clustering separates the field far more cleanly than the usual k-means baseline (silhouette 0.664 versus 0.483), whereas density-based methods reach higher scores only by discarding most of the corpus as noise. By separating rigorous analysis from public storytelling, BioArtlas turns the tangled complexity of bioart into a navigable landscape, openly available as an interactive interface (https://www.bioartlas.com) and dataset (https://github.com/joonhyungbae/BioArtlas).

(Source: arXiv:2511.19162)

## Key facts

- 81 bioartworks × 13 interpretive axes; shared concept codebook
- Agglomerative clustering silhouette 0.664 vs. k-means 0.483 (among methods that keep every work)
- Open interactive interface (bioartlas.com) and dataset (GitHub)
- NeurIPS 2025 Creative AI Track

## When this paper is relevant

- You classify or curate bioart / art–science works / 바이오아트·과학예술 작품을 분류하거나 큐레이션하는 방법을 찾을 때
- You map an art collection with multi-dimensional embeddings and clustering (digital art history, cultural analytics) / 예술 작품 컬렉션을 다차원 임베딩·군집으로 지도화(digital art history, cultural analytics)할 때
- You need to reconcile inconsistent keyword vocabularies with a shared codebook / 불일치하는 키워드·용어를 공유 코드북으로 정리하는 방법이 필요할 때
- You balance statistical cluster quality against interpretability / 통계적 군집 품질과 해석 가능성 사이의 균형을 다룰 때
- You want an example separating rigorous analysis from a public-facing storytelling interface / 계산적 분석과 대중용 스토리텔링 인터페이스를 분리 설계하는 예가 필요할 때

## Resources

- BioArtlas interactive atlas: https://www.bioartlas.com
- Dataset (GitHub): https://github.com/joonhyungbae/BioArtlas

## How to cite

Joonhyung Bae (2025). BioArtlas: Computational Clustering of Multi-Dimensional Complexity in Bioart. Proc. 39th Annual Conference on Neural Information Processing Systems Creative AI Track, San Diego, CA, USA, Dec. 2-7, 2025. https://arxiv.org/abs/2511.19162

```bibtex
@inproceedings{Bae2025bioartlas,
  title = {BioArtlas: Computational Clustering of Multi-Dimensional Complexity in Bioart},
  author = {Joonhyung Bae},
  booktitle = {Proc. 39th Annual Conference on Neural Information Processing Systems Creative AI Track, San Diego, CA, USA, Dec. 2-7, 2025},
  year = {2025},
  eprint = {2511.19162},
  archivePrefix = {arXiv},
  url = {https://arxiv.org/abs/2511.19162}
}
```